Determining Cochlear Implant Candidacy in Adults: Limitations, Expansions, and Opportunities for Improvement

Zwolan, Teresa A.; Basura, Gregory J.

doi:10.1055/s-0041-1739283

Cited by 24 publications

(21 citation statements)

References 36 publications

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“…Lack of clarity regarding referral criteria, both among providers and patients, is known to play a large factor in underutilization of this technology (15,(18)(19)(20)(21). Moreover, the absence of standardized referral guidelines that accommodate expanding indications and the variability in candidacy testing protocols across centers (e.g., incorporation of background noise) further complicate the landscape (12,22,23).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Patro

Perkins

Ortega³

et al. 2023

Otol Neurotol

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Objective To develop a machine learning–based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline. Study Design Retrospective cohort. Setting Tertiary referral center. Patients 772 adults undergoing CICE from 2015 to 2020. Interventions Variables included demographics, unaided thresholds, and word recognition score. A random forest classification model was trained on patients undergoing CICE, and bootstrap cross-validation was used to assess the modeling approach's performance. Main Outcome Measures The machine learning–based referral tool was evaluated against the 60/60 guideline based on ability to identify CI candidates under traditional and expanded criteria. Results Of 587 patients with complete data, 563 (96%) met candidacy at our center, and the 60/60 guideline identified 512 (87%) patients. In the random forest model, word recognition score; thresholds at 3000, 2000, and 125; and age at CICE had the largest impact on candidacy (mean decrease in Gini coefficient, 2.83, 1.60, 1.20, 1.17, and 1.16, respectively). The 60/60 guideline had a sensitivity of 0.91, a specificity of 0.42, and an accuracy of 0.89 (95% confidence interval, 0.86–0.91). The random forest model obtained higher sensitivity (0.96), specificity (1.00), and accuracy (0.96; 95% confidence interval, 0.95–0.98). Across 1,000 bootstrapped iterations, the model yielded a median sensitivity of 0.92 (interquartile range [IQR], 0.85–0.98), specificity of 1.00 (IQR, 0.88–1.00), accuracy of 0.93 (IQR, 0.85–0.97), and area under the curve of 0.96 (IQR, 0.93–0.98). Conclusions A novel machine learning–based screening model is highly sensitive, specific, and accurate in predicting CI candidacy. Bootstrapping confirmed that this approach is potentially generalizable with consistent results.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Patro

Perkins

Ortega³

et al. 2023

Otol Neurotol

View full text Add to dashboard Cite

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“…Current indications for adult implantation vary among manufacturers based on the Food and Drug Administration (FDA) insert labeling for each device (12). The Centers for Medicare and Medicaid Services (CMS) has more restrictive criteria, specifying up to 40% open-set sentence scores, whereas private payers permit up to 60% sentence scores in the best-sided listening condition.…”

Section: Introductionmentioning

confidence: 99%

A Five-Year Update on the Profile of Adults Undergoing Cochlear Implant Evaluation and Surgery—Are We Doing Better?

et al. 2022

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ObjectiveTo characterize the influence of expanding indications on the profile of adults undergoing cochlear implantation (CI) at a high-volume CI center.Study DesignRetrospective review.SettingTertiary referral center.Patients774 adults undergoing CI evaluation from August 2015 to August 2020.Main Outcome MeasuresDemographics; audiometry; speech recognition; speech, spatial, and qualities of hearing scale (SSQ-12).ResultsOf 745 (96.3%) patients qualifying for implantation, 642 (86.6%) pursued surgery. Median age at evaluation was 69 years; 56.3% were men; 88.2% were Caucasian. Median distance to our center was 95 miles. The majority (51.8%) had public insurance (Medicare, Medicaid), followed by private (47.8%) and military (0.4%). Mean PTA, CNC, and AzBio in quiet and noise for the ear to be implanted were 85.2 dB HL, 15.0%, and 19.2% and 3.5%, respectively. Hybrid/EAS criteria were met by 138 (18.5%) CI candidates, and 436 (77.0%) unilateral CI recipients had aidable contralateral hearing for bimodal hearing configurations. Younger age (odds ratio [OR], 0.96; 95% confidence interval, 0.93–0.99) and non-Caucasian race (OR, 6.95; 95% confidence interval, 3.22–14.98) predicted candidacy. Likelihood of surgery increased for Caucasian (OR, 8.08; 95% confidence interval, 4.85–13.47) and married (OR, 2.28; 95% confidence interval, 1.50–3.47) patients and decreased for those with public insurance (OR, 0.45; 95% confidence interval, 0.29–0.69). A lower SSQ-12 score predicted both candidacy and surgery.ConclusionDespite expansions in criteria, speech understanding remained extremely low at CI evaluation. Younger age and non-Caucasian race predicted candidacy, and Caucasian, married patients with private insurance and lower SSQ scores were more likely to pursue surgery.

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“…Any trauma can lead to immediate hearing loss or result in chronic inflammation, fibrosis and delayed loss of residual hearing 4 . Therefore, risk-benefit consideration often favors late implantations in patients with residual hearing 9 . However, aside from the challenge of living with hearing disability, postponing of CI surgery leads to further deterioration of the hearing nerve in the absence of sensorial input, leaving less opportunity for improvement when CI surgery is finally performed 9 .…”

Section: Introductionmentioning

confidence: 99%

An optically-guided cochlear implant sheath for real-time monitoring of electrode insertion into the human cochlea

Starovoyt

Quirk

Putzeys

et al. 2022

Sci Rep

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In cochlear implant surgery, insertion of perimodiolar electrode arrays into the scala tympani can be complicated by trauma or even accidental translocation of the electrode array within the cochlea. In patients with partial hearing loss, cochlear trauma can not only negatively affect implant performance, but also reduce residual hearing function. These events have been related to suboptimal positioning of the cochlear implant electrode array with respect to critical cochlear walls of the scala tympani (modiolar wall, osseous spiral lamina and basilar membrane). Currently, the position of the electrode array in relation to these walls cannot be assessed during the insertion and the surgeon depends on tactile feedback, which is unreliable and often comes too late. This study presents an image-guided cochlear implant device with an integrated, fiber-optic imaging probe that provides real-time feedback using optical coherence tomography during insertion into the human cochlea. This novel device enables the surgeon to accurately detect and identify the cochlear walls ahead and to adjust the insertion trajectory, avoiding collision and trauma. The functionality of this prototype has been demonstrated in a series of insertion experiments, conducted by experienced cochlear implant surgeons on fresh-frozen human cadaveric cochleae.

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Determining Cochlear Implant Candidacy in Adults: Limitations, Expansions, and Opportunities for Improvement

Cited by 24 publications

References 36 publications

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

A Five-Year Update on the Profile of Adults Undergoing Cochlear Implant Evaluation and Surgery—Are We Doing Better?

An optically-guided cochlear implant sheath for real-time monitoring of electrode insertion into the human cochlea

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